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<!-- ==================== CLASS DESCRIPTION ==================== -->
<h1 class="epydoc">Class Model</h1><p class="nomargin-top"><span class="codelink"><a href="BIP.SMC.condensation-pysrc.html#Model">source&nbsp;code</a></span></p>
<pre class="base-tree">
object --+
         |
        <strong class="uidshort">Model</strong>
</pre>

<hr />
Example Model Specification

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          <td><span class="summary-sig"><a href="BIP.SMC.condensation.Model-class.html#__init__" class="summary-sig-name">__init__</a>(<span class="summary-sig-arg">self</span>)</span><br />
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          <td><span class="summary-sig"><a name="setupPriorConditions"></a><span class="summary-sig-name">setupPriorConditions</span>(<span class="summary-sig-arg">self</span>)</span></td>
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          <td><span class="summary-sig"><a href="BIP.SMC.condensation.Model-class.html#iterate" class="summary-sig-name">iterate</a>(<span class="summary-sig-arg">self</span>,
        <span class="summary-sig-arg">previous</span>,
        <span class="summary-sig-arg">process</span>)</span><br />
      The process model for a first-order auto-regressive process is:</td>
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            <span class="codelink"><a href="BIP.SMC.condensation-pysrc.html#Model.iterate">source&nbsp;code</a></span>
            
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          <td><span class="summary-sig"><a href="BIP.SMC.condensation.Model-class.html#predictSamplePosition" class="summary-sig-name">predictSamplePosition</a>(<span class="summary-sig-arg">self</span>,
        <span class="summary-sig-arg">new_sample</span>,
        <span class="summary-sig-arg">old_sample</span>)</span><br />
      This routine samples from the distribution</td>
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      <span class="summary-type">&nbsp;</span>
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          <td><span class="summary-sig"><a href="BIP.SMC.condensation.Model-class.html#evaluateObservationDensity" class="summary-sig-name">evaluateObservationDensity</a>(<span class="summary-sig-arg">self</span>,
        <span class="summary-sig-arg">new_sample</span>)</span><br />
      This routine evaluates the observation density</td>
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          <td><span class="summary-sig"><a href="BIP.SMC.condensation.Model-class.html#obtainObservations" class="summary-sig-name">obtainObservations</a>(<span class="summary-sig-arg">self</span>)</span><br />
      In a real implementation, this routine would go and actually make
measurements and store them in the data.meas structure.</td>
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            <span class="codelink"><a href="BIP.SMC.condensation-pysrc.html#Model.obtainObservations">source&nbsp;code</a></span>
            
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          <td><span class="summary-sig"><a name="display"></a><span class="summary-sig-name">display</span>(<span class="summary-sig-arg">self</span>,
        <span class="summary-sig-arg">iteration</span>)</span></td>
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    <p class="indent-wrapped-lines"><b>Inherited from <code>object</code></b>:
      <code>__delattr__</code>,
      <code>__format__</code>,
      <code>__getattribute__</code>,
      <code>__hash__</code>,
      <code>__new__</code>,
      <code>__reduce__</code>,
      <code>__reduce_ex__</code>,
      <code>__repr__</code>,
      <code>__setattr__</code>,
      <code>__sizeof__</code>,
      <code>__str__</code>,
      <code>__subclasshook__</code>
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      <code>__class__</code>
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<a name="__init__"></a>
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  <h3 class="epydoc"><span class="sig"><span class="sig-name">__init__</span>(<span class="sig-arg">self</span>)</span>
    <br /><em class="fname">(Constructor)</em>
  </h3>
  </td><td align="right" valign="top"
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  <p>x.__init__(...) initializes x; see x.__class__.__doc__ for 
  signature</p>
  <dl class="fields">
    <dt>Overrides:
        object.__init__
        <dd><em class="note">(inherited documentation)</em></dd>
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  <h3 class="epydoc"><span class="sig"><span class="sig-name">iterate</span>(<span class="sig-arg">self</span>,
        <span class="sig-arg">previous</span>,
        <span class="sig-arg">process</span>)</span>
  </h3>
  </td><td align="right" valign="top"
    ><span class="codelink"><a href="BIP.SMC.condensation-pysrc.html#Model.iterate">source&nbsp;code</a></span>&nbsp;
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  <p>The process model for a first-order auto-regressive process is:</p>
<p>x_{t+1} - mean = (x_t - mean)*scaling + sigma*w_t</p>
<p>where w_t is unit iid Gaussian noise.</p>
  <dl class="fields">
    <dt>Parameters:</dt>
    <dd><ul class="nomargin-top">
        <li><strong class="pname"><code>previous</code></strong> - previous data</li>
        <li><strong class="pname"><code>process</code></strong> - processmodel parameter tuple</li>
    </ul></dd>
  </dl>
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<a name="predictSamplePosition"></a>
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  <h3 class="epydoc"><span class="sig"><span class="sig-name">predictSamplePosition</span>(<span class="sig-arg">self</span>,
        <span class="sig-arg">new_sample</span>,
        <span class="sig-arg">old_sample</span>)</span>
  </h3>
  </td><td align="right" valign="top"
    ><span class="codelink"><a href="BIP.SMC.condensation-pysrc.html#Model.predictSamplePosition">source&nbsp;code</a></span>&nbsp;
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  <p>This routine samples from the distribution</p>
<p>p(x_t | x_{t-1} = oldpos[old_sample])</p>
<p>and stores the result in new_positions[new_sample]. This is
straightforward for the simple first-order auto-regressive process
model used here, but any model could be substituted.</p>
  <dl class="fields">
  </dl>
</td></tr></table>
</div>
<a name="evaluateObservationDensity"></a>
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  <h3 class="epydoc"><span class="sig"><span class="sig-name">evaluateObservationDensity</span>(<span class="sig-arg">self</span>,
        <span class="sig-arg">new_sample</span>)</span>
  </h3>
  </td><td align="right" valign="top"
    ><span class="codelink"><a href="BIP.SMC.condensation-pysrc.html#Model.evaluateObservationDensity">source&nbsp;code</a></span>&nbsp;
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  <p>This routine evaluates the observation density</p>
<p>p(z_t|x_t = newpos[new_sample])</p>
<p>The observation model in this implementation is a simple mixture of
Gaussians, where each simulated object is observed as a 1d position
and measurement noise is represented as Gaussian. For a
visual-tracking application, this routine would go and evaluate the
likelihood that the object is present in the image at the position
encoded by new_positions[new_sample].</p>
  <dl class="fields">
  </dl>
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<a name="obtainObservations"></a>
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  <h3 class="epydoc"><span class="sig"><span class="sig-name">obtainObservations</span>(<span class="sig-arg">self</span>)</span>
  </h3>
  </td><td align="right" valign="top"
    ><span class="codelink"><a href="BIP.SMC.condensation-pysrc.html#Model.obtainObservations">source&nbsp;code</a></span>&nbsp;
    </td>
  </tr></table>
  
  In a real implementation, this routine would go and actually make
measurements and store them in the data.meas structure. This
simulation consists of an object moving around obeying a
first-order auto-regressive process, and being observed with its
true positions coorrupted by Gaussian measurement noise.
Accordingly, this routine calculates the new simulated true and
measured position of the object.
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